This Week's AI Executive Playbook

⏱️ Your Friday Brief (TL;DR)

Welcome back!

Your competition is either building AI agents or talking about building AI agents. This week, we've got the playbook that separates the two: OpenAI's internal productivity secrets, McKinsey's year-by-year agent transformation guide, and why "context engineering" is the new skill your team needs yesterday.

Let’s dive in 🤖

🚀 How OpenAI uses OpenAI

The GPTLDR

OpenAI on OpenAI is a new series showing how OpenAI uses AI internally. The goal is to share patters that other enterprises can adopt, starting with five production AI solutions that address key business challenges.

The Details

• GTM Assistant: A Slack-based tool streamlines research, meetings, and product Q&A, improving average sales rep productivity by 20%.

• DocuGPT: Converts contracts into structured, searchable data, helping finance teams review faster, more consistently, and at scale.

• Research Assistant: Turns millions of support tickets into conversational insights, surfacing trends to help act on customer feedback quickly.

• Support Agent: Uses AI agents, continuous evals, and dynamic knowledge loops to turn every interaction into training data and improve quality.

• Inbound Sales Assistant: Personalizes lead responses, raising accuracy from 60% to over 98% within weeks, and routes qualified prospects.

Why It Matters:

• OpenAI faces the same operational headaches you do—they just fixed theirs with AI

• These aren't moonshot projects; they're pragmatic solutions with measurable ROI

• If the AI company is betting their own operations on this tech, maybe it's time you did too.

🎯 McKinsey’s Two-year Agentic AI Journey

The GPTLDR

McKinsey mapped out your agent transformation timeline. Spoiler: Year one is messy, year two gets interesting, and the CEOs who start now eat everyone else's lunch by 2027.

The Details

• Reality check: Most execs still think agents are chatbots with attitude. McKinsey breaks them down by what they actually do—from simple automation to cross-functional orchestration

• Year One: The awkward teenage phase. Agents start handling basic processes, some roles shift, everyone freaks out a little. Normal.

• Year Two: Now we're cooking. P&L impact replaces vanity metrics. Agent-to-human ratios become a KPI. The org chart starts looking different.

• The CEO Juggle: Run two playbooks simultaneously—quick wins today, transformation tomorrow. Think iPhone while still selling iPods.

• Board conversations shift from "what's our AI strategy?" to "how fast can we scale this?"

Why It's Important:

• The "trough of disillusionment" is your competitive moat—while others wait, you build

• First movers aren't experimenting anymore; they're shipping at scale

• Two years sounds long until your competitor announces their "AI-first" transformatio

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🤖 Prompt vs. Context Engineering for AI Agents

The GPTLDR: Anthropic just pushed the effectiveness of prompt engineering. The new game is context engineering—curating exactly what your AI needs to know, when it needs to know it. Less word-smithing, more strategic information architecture.

The Details

• The shift: Stop obsessing over magic prompt phrases. Start thinking about the minimum viable context for maximum output

• Token economics: Every word costs attention. Smart context = better results with less overhead

• Techniques that matter: Compaction for long tasks, token-efficient tool design, just-in-time information loading

• The paradox: Smarter models need less hand-holding but still require surgical precision with context

• Bottom line: Even GPT-7 won't save you from bad context management

Why It’s Important:

• Shopify's CEO called it in June, Andrej Karpathy amplified it—this is where the industry's headed

• Your prompt library from 2024? Probably obsolete. Your context strategy? That's your moat

• This isn't academic—it's the difference between agents that work and agents that burn compute

📚 Interesting Reads

• Anthropic: How enterprises are driving AI transformation with Claude

• Deloitte’s framework for qualifying and prioritizing Agentic AI use cases

• MIT Sloan: How LLMs work - Top 10 Executive-Level questions

• How AI agents work, case studies by C3 AI

• BCG breaksdown how AI leaders are driving revenue growth and savings

 ➜ Until Next Week

Stay curious,

A counterintuitive way to end the newsletter, but a great reminder for ourselves even here at GPTLDR. Stop reading. Start building. Your agents won't train themselves

The GPTLDR Team

AI, simplified for Decision Makers.